The integration of digital tools and Artificial Intelligence (AI) has brought
significant advancements to the diagnosis of various diseases, particularly
neurodegenerative disorders like Alzheimer’s disease. AI, particularly Machine
Learning (ML), enables the analysis of vast and complex datasets, such as
neuroimaging, electronic health records, cognitive assessments, and biomarkers, which
are crucial for early detection and accurate diagnosis. These technologies offer the
potential to identify subtle changes in patients' conditions over time, improving the
precision of diagnosis and facilitating the development of personalized treatment plans.
Digital tools, including wearable devices, mobile applications, and fitness trackers,
allow for continuous and passive monitoring of patient data, providing real-time
insights into physiological parameters like gait, heart rate, and brain activity. These
tools can detect early signs of cognitive decline and other abnormalities, aiding
clinicians in making more informed decisions about disease progression. AI has
demonstrated its potential in automating image analysis, identifying biomarkers, and
predicting disease progression, which can expedite the diagnostic process and enhance
the overall healthcare experience. Additionally, AI-driven models can integrate data
from multiple sources, enabling comprehensive assessments and more accurate
predictions about the onset and development of diseases like AD. However, there are
several challenges associated with the widespread adoption of AI in clinical settings,
such as concerns about data quality, model interpretability, ethical considerations, and
cost. Overcoming these barriers is essential to ensuring that AI technologies become
accessible, reliable, and effective in healthcare. This chapter explores the
transformative potential of AI and digital tools in diagnostics, discussing their
applications, benefits, challenges, and the future directions for improving patient care
and outcomes.
Keywords: Alzheimer’s disease, Artificial Intelligence, Biomarkers, Digital tools, Early diagnosis, Machine Learning, Neuroimaging.
